Only load global step when fitting

src/pytorch_lightning/CHANGELOG.md

@@ -21,7 +21,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - From now on, Lightning Trainer and `LightningModule.load_from_checkpoint` automatically upgrade the loaded checkpoint if it was produced in an old version of Lightning ([#15237](https://github.com/Lightning-AI/lightning/pull/15237))
 
--
+- `Trainer.{validate,test,predict}(ckpt_path=...)` no longer restores the `Trainer.global_step` and `trainer.current_epoch` value from the checkpoints - From now on, only `Trainer.fit` will restore this value ([#15532](https://github.com/Lightning-AI/lightning/pull/15532))
 
 -
 

src/pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -341,19 +341,20 @@ def restore_loops(self) -> None:
         pl_module = self.trainer.lightning_module
         assert pl_module is not None
 
-        # set the `global_step` value for checkpoints before v1.6 without the progress tracking state.
-        # it will be overwritten by the loop's state if it was also saved
-        batch_loop = fit_loop.epoch_loop.batch_loop
-        if pl_module.automatic_optimization:
-            batch_loop.optimizer_loop.optim_progress.optimizer.step.total.completed = self._loaded_checkpoint[
-                "global_step"
-            ]
-        else:
-            batch_loop.manual_loop.optim_step_progress.total.completed = self._loaded_checkpoint["global_step"]
-
-        # set the `current_epoch` value for checkpoints before v1.6 without the progress tracking state.
-        # it will be overwritten by the loop's state if it was also saved
-        fit_loop.epoch_progress.current.completed = self._loaded_checkpoint["epoch"]
+        if self.trainer.state.fn == TrainerFn.FITTING:
+            # set the `global_step` value for checkpoints before v1.6 without the progress tracking state.
+            # it will be overwritten by the loop's state if it was also saved
+            batch_loop = fit_loop.epoch_loop.batch_loop
+            if pl_module.automatic_optimization:
+                batch_loop.optimizer_loop.optim_progress.optimizer.step.total.completed = self._loaded_checkpoint[
+                    "global_step"
+                ]
+            else:
+                batch_loop.manual_loop.optim_step_progress.total.completed = self._loaded_checkpoint["global_step"]
+
+            # set the `current_epoch` value for checkpoints before v1.6 without the progress tracking state.
+            # it will be overwritten by the loop's state if it was also saved
+            fit_loop.epoch_progress.current.completed = self._loaded_checkpoint["epoch"]
 
         assert self.trainer.state.fn is not None
         state_dict = self._loaded_checkpoint.get("loops")

tests/tests_pytorch/models/test_restore.py

@@ -187,7 +187,7 @@ def _check_model_state_dict(self):
 
         def _test_on_val_test_predict_start(self):
             assert self.trainer.current_epoch == state_dict["epoch"]
-            assert self.trainer.global_step == state_dict["global_step"]
+            assert self.trainer.global_step == 0
             assert self._check_model_state_dict()
 
         def on_train_start(self):
@@ -626,8 +626,10 @@ def __init__(self):
             super().__init__()
             self.on_train_start_called = False
 
-        def on_validation_start(self):
+        def on_train_start(self):
             assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0
+
+        def on_validation_start(self):
             dataloader = dm.val_dataloader()
             tpipes.run_model_prediction(self.trainer.lightning_module, dataloader=dataloader)